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Estimating bicycle trip volume for Miami-Dade county from Strava tracking data

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  • Hochmair, Hartwig H.
  • Bardin, Eric
  • Ahmouda, Ahmed

Abstract

Sports and fitness apps on GPS enabled cell phones and smart watches have become a rich source of GPS tracking data for nonmotorized traffic, including walking, running, and cycling. These crowd-sourced data can be analyzed to better understand the cycling behavior of a large user community. Using Strava tracking data from the Miami-Dade County area, this study identifies which transport network measures, characteristics of the built environment, and sociodemographic factors are associated with increased or decreased bicycle ridership in census block groups. For this purpose, a set of linear regression models are estimated to predict non-commute and commute bicycle kilometers travelled per block group, as well as bicycle kilometers travelled on weekends and weekdays. Eigenvector spatial filtering is applied to explicitly model spatial autocorrelation and to avoid parameter estimation bias. Results suggest that Strava data, due to its high spatial resolution and coverage, can identify in detail how the influence of explanatory variables on estimated bicycle trip volume varies between different trip purposes and days of the week. Based on the regression results, the paper presents a set of guidelines for practical design detailing which groups of cyclists would benefit most from specific bicycle infrastructure improvements.

Suggested Citation

  • Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    • Handle: RePEc:eee:jotrge:v:75:y:2019:i:c:p:58-69
    DOI: 10.1016/j.jtrangeo.2019.01.013
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    Cited by:

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    2. Raturi, Varun & Hong, Jinhyun & McArthur, David Philip & Livingston, Mark, 2021. "The impact of privacy protection measures on the utility of crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 92(C).
    3. Tineke de Jong & Lars Böcker & Christian Weber, 2023. "Road infrastructures, spatial surroundings, and the demand and route choices for cycling: Evidence from a GPS-based mode detection study from Oslo, Norway," Environment and Planning B, , vol. 50(8), pages 2133-2150, October.
    4. Parisa Zare & Christopher Pettit & Simone Leao & Ori Gudes, 2022. "Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
    5. Hong, Jinhyun & Philip McArthur, David & Stewart, Joanna L., 2020. "Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 109-121.
    6. Stella R. Harden & Nadine Schuurman & Peter Keller & Scott A. Lear, 2022. "Neighborhood Characteristics Associated with Running in Metro Vancouver: A Preliminary Analysis," IJERPH, MDPI, vol. 19(21), pages 1-13, November.
    7. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    8. Munira, Sirajum & Sener, Ipek N., 2020. "A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas," Journal of Transport Geography, Elsevier, vol. 88(C).
    9. Alattar, Mohammad Anwar & Cottrill, Caitlin & Beecroft, Mark, 2021. "Public participation geographic information system (PPGIS) as a method for active travel data acquisition," Journal of Transport Geography, Elsevier, vol. 96(C).

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